Data/hora: 12/06/2024 13:41:32

Provedor de dados: | 31
País: | United States
Título: | Minimum Divergence Moment Based Binary Response Models: Estimation and Inference
Autores: | Mittelhammer,   Ronald C.; Judge,   George G.; Miller,   Douglas J.; Cardell,   N. Scott.
Data: | 2006-02-06
Ano: | 2005
Palavras-chave: | Nonparametric binary response models and estimators; Conditional moment equations; Finite sample bias and precision; Squared error loss; Response variables; Cressie-Read statistic; Information theoretic methods; Research Methods/ Statistical Methods; C10; C2.
Resumo: | This paper introduces a new class of estimators based on minimization of the Cressie-Read (CR) power divergence measure for binary choice models, where neither a parameterized distribution nor a parameterization of the mean is specified explicitly in the statistical model. By incorporating sample information in the form of conditional moment conditions and estimating choice probabilities by optimizing a member of the set of divergence measures in the CR family, a new class of nonparametric estimators evolves that requires less a priori model structure than conventional parametric estimators such as probit or logit. Asymptotic properties are derived under general regularity conditions and finite sampling properties are illustrated by Monte Carlo sampling experiments. Except for some special cases in which the general regularity conditions do not hold, the estimators have asymptotic normal distributions, similar to conventional parametric estimators of the binary choice model. The sampling experiments focus on the mean square errors in the choice probability predictions and the probability derivatives with respect to the response variable values. The simulation results suggest that estimators within the CR class are more robust than conventional methods of estimation across varying probability distributions underlying the Bernoulli process. The size and power of test statistics based on the asymptotics of the CR-based estimators exhibit behavior similar to those based on conventional parametric methods. Overall, the new class of nonparametric estimators for the binary response model is a promising and potentially more robust alternative to the parametric methods often used in empirical practice.
Tipo: | Working or Discussion Paper
Idioma: | Inglês

Identificador:

19751

http://purl.umn.edu/25020

Editor: | AgEcon Search

Relação:

University of California, Berkeley&gt;Department of Agricultural and Resource Economics&gt;CUDARE Working Papers

CUDARE Working Paper 998

Formato:

43

application/pdf